Integrating artificial intelligence into vehicles

ABSTRACT

Systems and methods may be used for vehicle support or operation. A method may be performed using an edge device to support operations of a vehicle. The method may include receiving a request from a vehicle component to register with an artificial intelligence processing component of the edge device, sending an acknowledgement of the registration to the vehicle component, receiving a request for a service of the artificial intelligence processing component, and providing, to the vehicle component in response to the request, a response from the service.

CLAIM OF PRIORITY

This application claims the benefit of priority to U.S. ProvisionalApplication Ser. No. 63/018,194, filed Apr. 30, 2020, titled“INTEGRATING ARTIFICIAL INTELLIGENCE INTO AUTONOMOUS VEHICLES,” which ishereby incorporated herein by reference in its entirety.

BACKGROUND

Edge computing, at a general level, refers to the transition of computeand storage resources closer to endpoint devices (e.g., consumercomputing devices, user equipment, etc.) in order to optimize total costof ownership, reduce application latency, improve service capabilities,and improve compliance with security or data privacy requirements. Edgecomputing may, in some scenarios, provide a cloud-like distributedservice that offers orchestration and management for applications amongmany types of storage and compute resources. As a result, someimplementations of edge computing have been referred to as the “edgecloud” or the “fog”, as powerful computing resources previouslyavailable only in large remote data centers are moved closer toendpoints and made available for use by consumers at the “edge” of thenetwork.

Edge computing use cases in mobile network settings have been developedfor integration with artificial intelligence techniques. Limitedstandards have been developed by the European TelecommunicationsStandards Institute (ETSI) Experiential Networked intelligence (ENI)industry specification group (ISG) in an attempt to define commoninterfaces for operation of artificial intelligence systems, platforms,hosts, services, and applications.

Edge computing and related technologies attempt to provide reducedlatency, increased responsiveness, and more available computing powerthan offered in traditional cloud network services and wide area networkconnections. However, the integration of mobility and dynamicallylaunched services to some mobile use and device processing use cases hasled to limitations and concerns with orchestration, functionalcoordination, and resource management, especially in complex mobilitysettings where many participants (devices, hosts, tenants, serviceproviders, operators) are involved.

BRIEF DESCRIPTION OF THE DRAWINGS

In the drawings, which are not necessarily drawn to scale, like numeralsmay describe similar components in different views. Like numerals havingdifferent letter suffixes may represent different instances of similarcomponents. Some embodiments are illustrated by way of example, and notlimitation, in the figures of the accompanying drawings in which:

FIG. 1 illustrates an overview of an edge cloud configuration or edgecomputing, according to an example;

FIG. 2 illustrates a vehicle compute and communication use caseinvolving mobile access to applications in an edge computing system,according to an example;

FIG. 3A illustrates an overview of example components deployed at acompute node system, according to an example;

FIG. 3B illustrates a further overview of example components within acomputing device, according to an example;

FIG. 4 illustrates example components of an autonomous driving system,according to an example;

FIG. 5 illustrates an example networked intelligence architecture,according to an example;

FIG. 6 illustrates an example perception component connection diagram,according to an example;

FIG. 7 illustrates an example decision and control component connectiondiagram, according to an example;

FIG. 8 illustrates an example vehicle platform manipulation componentconnection diagram, according to an example;

FIG. 9 illustrates an example component tagging connection diagram,according to an example;

FIG. 10 illustrates an example tagging data structure, according to anexample;

FIG. 11 illustrates a flowchart of an example process for interfacing anetworked intelligence system with vehicle components, according to anexample; and

FIG. 12 illustrates a flowchart of an example process for tagging orproviding tagging information related to a security profile ofcomponents of a networked architecture, according to an example.

DETAILED DESCRIPTION

In the following description, methods, configurations, and relatedapparatuses are disclosed for integrating components or subsystems ofAutonomous Vehicles (AV) with artificial intelligence (AI) components orsubsystems based on the ETSI ENI framework. In previous solutions, an AIframework was developed independently by each manufacturer in aproprietary fashion for its own AV technology. This leads to highercosts, longer development cycles and reusage issues if new vehiclegenerations build on different platform choices. The systems and methodsherein describe how to integrate AVs internal and external componentsand subsystems into the existing AI platform and framework proposed byETSI ENI, while expanding such frameworks and platforms for moreadvanced edge computing and autonomous vehicle use cases.

The systems and methods described herein relate to certificate taggingof components or subsystems for security. The security may includelevels, for example based on regulation (such as by the European Unionor the United States). Regulations may include a Radio EquipmentDirective (RED), which, in its Article 3, requires (e.g., split up insub-articles 3(3)(a) . . . 3(3)(i)) certain security measures be takenfor radio equipment to be permitted to the European market. Thesesecurity measures include requirements by Article 3(3)(e) on privacyrequirements (for data) and Article 3(3)(f) on fraud/cybersecurity, forexample. Another regulation may include an EU Cybersecurity Act, whichintroduces an EU-wide cybersecurity certification framework for ICTproducts, services and processes. The European Commission StakeholderCybersecurity Certification Group (SCCG) has been recently created todevelop related certification mechanisms.

Any component or subsystem (e.g., radio or processing) of an AutonomousVehicle (AV) may relate to one or more of the requirements of the RED orthe Cybersecurity Act. The systems and methods described herein providea tag for AV components and subsystems. The tag may be used to verifywhich AV component or subsystem meets which RED/Cybersecurity Actrequirements and whether the AV component or subsystem meets allRED/Cybersecurity Act requirements. Also, this tagging helps to identifywhether some AV components or sub-systems may violate Europeanrequirements although they are allowed in other regions (such asUS/Asia, e.g., the national institute of standards and technology (NIST)in the US, such as the NIST Cyber Security Framework, or region specifictagging, national specific tagging, etc.). Those components or subsystemmust be de-activated.

Certification for RED or the Cybersecurity Act may include: RED relatedcertification (in particular fraud/cybersecurity related certificationin relation to RED Article 3(3)(f)), including RED certification relatedto RED Article 3(3)(f) on fraud/cybersecurity is expected to requirecertain hardware features, typically including requirements forprotected memory, requirements for a Trusted Platform Module (TPM),requirements for Operating Systems (e.g., specific robust exceptionhandling mechanisms, etc.), etc. Certification for Cybersecurity Act mayinclude: certification related to the usage of hardware componentsmandated by the RED, for example: which encryption key(s) should besaved in a TPM and how they should be accessed/used, which protocolshould be used for secure exchange of data that may relate to theprivacy of the user, or the like.

FIG. 1 is a block diagram 100 showing an overview of a configuration foredge computing, which includes a layer of processing referenced in manyof the current examples as an “edge cloud”. This network topology, whichmay include a number of conventional networking layers (including thosenot shown herein), may be extended through use of the AI platform ordata tagging techniques and configurations discussed herein.

As shown, the edge cloud 110 is co-located at an edge location, such asthe base station 140, a local processing hub 150, or a central office120, and thus may include multiple entities, devices, and equipmentinstances. The edge cloud 110 is located much closer to the endpoint(consumer and producer) data sources 160 (e.g., vehicles 161, userequipment 162, business and industrial equipment 163, video capturedevices 164, drones 165, smart cities and building devices 166, sensorsand IoT devices 167, etc.) than the cloud data center 130. In anexample, vehicles 161 may include an autonomous vehicle, such as avehicle that includes one or more components that aid a driver (e.g.,driver support features) or are autonomous, acting without driverattention (e.g., automated driving features). These components mayinclude various levels based on degree of automation. Driver supportfeatures may include features that provide alerts or minor or briefchanges to vehicle operations, such as lane alerts, adaptive cruisecontrol, blind spot warning, automatic braking (e.g., emergency or inback-up mode), or the like. Automated driving features may includetraffic routing, entirely driverless vehicles or modes, or the like.

Compute, memory, and storage resources which are offered at the edges inthe edge cloud 110 are critical to providing ultra-low latency responsetimes for services and functions used by the endpoint data sources 160as well as reduce network backhaul traffic from the edge cloud 110toward cloud data center 130 thus improving energy consumption andoverall network usages among other benefits.

Compute, memory, and storage are scarce resources, and generallydecrease depending on the edge location (e.g., fewer processingresources being available at consumer end point devices than at a basestation or at a central office). However, the closer that the edgelocation is to the endpoint (e.g., UEs), the more that space and poweris constrained. Thus, edge computing, as a general design principle,attempts to minimize the amount of resources needed for networkservices, through the distribution of more resources which are locatedcloser both geographically and in network access time.

The following involves aspects of an edge cloud architecture that coversmultiple potential deployments and addresses restrictions that somenetwork operators or service providers may have in their owninfrastructures. These include, variation of configurations based on theedge location (because edges at a base station level, for instance, mayhave more constrained performance); configurations based on the type ofcompute, memory, storage, fabric, acceleration, or like resourcesavailable to edge locations, tiers of locations, or groups of locations;the service, security, and management and orchestration capabilities;and related objectives to achieve usability and performance of endservices.

Edge computing is a developing paradigm where computing is performed ator closer to the “edge” of a network, typically through the use of acompute platform implemented at base stations, gateways, networkrouters, or other devices which are much closer to end point devicesproducing and consuming the data. For example, edge gateway servers maybe equipped with pools of memory and storage resources to performcomputation in real-time for low latency use-cases (e.g., autonomousdriving or video surveillance) for connected client devices. Or as anexample, base stations may be augmented with compute and accelerationresources to directly process service workloads for connected userequipment, without further communicating data via backhaul networks. Oras another example, central office network management hardware may bereplaced with compute hardware that performs virtualized networkfunctions and offers compute resources for the execution of services andconsumer functions for connected devices. These and other scenarios mayinvolve the use of attestation, as provided in the discussion below.

In contrast to the network architecture of FIG. 1 , traditional endpointconnection (e.g., UE-to everything, vehicle-to-vehicle (V2V),vehicle-to-everything (V2X), etc.) applications are reliant on localdevice or remote cloud data storage and processing to exchange andcoordinate information. A cloud data arrangement allows for long-termdata collection and storage, but is not optimal for highly time varyingdata, such as a collision, traffic light change, etc. and may fail inattempting to meet latency challenges.

Depending on the real-time requirements in a communications context, ahierarchical structure of data processing and storage nodes may bedefined in an edge computing deployment. For example, such a deploymentmay include local ultra-low-latency processing, regional storage andprocessing as well as remote cloud data-center based storage andprocessing. Key performance indicators (KPIs) may be used to identifywhere sensor data is best transferred and where it is processed orstored. This typically depends on the ISO layer dependency of the data.For example, lower layer (PHY, MAC, routing, etc.) data typicallychanges quickly and is better handled locally in order to meet latencyrequirements. Higher layer data such as Application Layer data istypically less time critical and may be stored and processed in a remotecloud datacenter.

FIG. 2 shows a simplified vehicle compute and communication use caseinvolving mobile access to applications in an edge computing system 200that implements an edge cloud 110. In this use case, each client computenode 210 may be embodied as in-vehicle compute systems (e.g., in-vehiclenavigation and/or infotainment systems) located in correspondingvehicles that communicate with the edge gateway nodes 220 duringtraversal of a roadway. For instance, edge gateway nodes 220 may belocated in roadside cabinets, which may be placed along the roadway, atintersections of the roadway, or other locations near the roadway. Aseach vehicle traverses along the roadway, the connection between itsclient compute node 210 and a particular edge gateway node 220 maypropagate so as to maintain a consistent connection and context for theclient compute node 210. Each of the edge gateway nodes 220 includessome processing and storage capabilities and, as such, some processingand/or storage of data for the client compute nodes 210 may be performedon one or more of the edge gateway nodes 220.

Each of the edge gateway nodes 220 may communicate with one or more edgeresource nodes 240, which are illustratively embodied as computeservers, appliances or components located at or in a communication basestation 242 (e.g., a base station of a cellular network). As discussedabove, each edge resource node 240 includes some processing and storagecapabilities and, as such, some processing and/or storage of data forthe client compute nodes 210 may be performed on the edge resource node240. For example, the processing of data that is less urgent orimportant may be performed by the edge resource node 240, while theprocessing of data that is of a higher urgency or importance may beperformed by edge gateway devices or the client nodes themselves(depending on, for example, the capabilities of each component).

The edge resource node(s) 240 also communicate with the core data center250, which may include compute servers, appliances, and/or othercomponents located in a central location (e.g., a central office of acellular communication network). The core data center 250 may provide agateway to the global network cloud 260 (e.g., the Internet) for theedge cloud 110 operations formed by the edge resource node(s) 240 andthe edge gateway nodes 220. Additionally, in some examples, the coredata center 250 may include an amount of processing and storagecapabilities and, as such, some processing and/or storage of data forthe client compute devices may be performed on the core data center 250(e.g., processing of low urgency or importance, or high complexity). Theedge gateway nodes 220 or the edge resource nodes 240 may offer the useof stateful applications 232 and a geographic distributed data storage234 (e.g., database data store, etc.).

In further examples, FIG. 2 may utilize various types of mobile edgenodes, such as an edge node hosted in a vehicle (e.g., car, truck, tram,train, etc.) or other mobile unit, as the edge node will move to othergeographic locations along the platform hosting it. Withvehicle-to-vehicle communications, individual vehicles may even act asnetwork edge nodes for other cars, (e.g., to perform caching, reporting,data aggregation, etc.). Thus, it will be understood that theapplication components provided in various edge nodes may be distributedin a variety of settings, including coordination between some functionsor operations at individual endpoint devices or the edge gateway nodes220, some others at the edge resource node 240, and others in the coredata center 250 or global network cloud 260.

In further configurations, the edge computing system may implementFunction-as-a-Service (FaaS) computing capabilities through the use ofrespective executable applications and functions. In an example, adeveloper writes function code (e.g., “computer code” herein)representing one or more computer functions, and the function code isuploaded to a FaaS platform provided by, for example, an edge node ordata center. A trigger such as, for example, a service use case or anedge processing event, initiates the execution of the function code withthe FaaS platform.

In an example of FaaS, a container is used to provide an environment inwhich function code is executed. The container may be anyisolated-execution entity such as a process, a Docker or Kubernetescontainer, a virtual machine, etc. Within the edge computing system,various datacenter, edge, and endpoint (including mobile) devices areused to “spin up” functions (e.g., activate and/or allocate functionactions) that are scaled on demand. The function code gets executed onthe physical infrastructure (e.g., edge computing node) device andunderlying virtualized containers. Finally, the container is “spun down”(e.g., deactivated and/or deallocated) on the infrastructure in responseto the execution being completed.

Further aspects of FaaS may enable deployment of edge functions in aservice fashion, including a support of respective functions thatsupport edge computing as a service. Additional features of FaaS mayinclude: a granular billing component that enables customers (e.g.,computer code developers) to pay only when their code gets executed;common data storage to store data for reuse by one or more functions;orchestration and management among individual functions; functionexecution management, parallelism, and consolidation; management ofcontainer and function memory spaces; coordination of accelerationresources available for functions; and distribution of functions betweencontainers (including “warm” containers, already deployed or operating,versus “cold” which require deployment or configuration).

In further examples, any of the compute nodes or devices discussed withreference to the present edge computing systems and environment may befulfilled based on the components depicted in FIGS. 3A and 3B. Each edgecompute node may be embodied as a type of device, appliance, computer,or other “thing” capable of communicating with other edge, networking,or endpoint components. For example, an edge compute device may beembodied as a smartphone, a mobile compute device, a smart appliance, anin-vehicle compute system (e.g., a navigation system), a road-side unit,a base station, a server, a gateway, or other device or system capableof performing the described functions.

In the simplified example depicted in FIG. 3A, an edge compute node 300includes a compute engine (also referred to herein as “computecircuitry”) 302, an input/output (I/O) subsystem 308, data storage 310,a communication circuitry subsystem 312, and, optionally, one or moreperipheral devices 314. In other examples, each compute device mayinclude other or additional components, such as those used in personalor server computing systems (e.g., a display, peripheral devices, etc.).Additionally, in some examples, one or more of the illustrativecomponents may be incorporated in, or otherwise form a portion of,another component.

The compute node 300 may be embodied as any type of engine, device, orcollection of devices capable of performing various compute functions.In some examples, the compute node 300 may be embodied as a singledevice such as an integrated circuit, an embedded system, afield-programmable gate array (FPGA), a system-on-a-chip (SOC), or otherintegrated system or device. In the illustrative example, the computenode 300 includes or is embodied as a processor 304 and a memory 306.The processor 304 may be embodied as any type of processor capable ofperforming the functions described herein (e.g., executing anapplication). For example, the processor 304 may be embodied as amulti-core processor(s), a microcontroller, or other processor orprocessing/controlling circuit. In some examples, the processor 304 maybe embodied as, include, or be coupled to an FPGA, an applicationspecific integrated circuit (ASIC), reconfigurable hardware or hardwarecircuitry, or other specialized hardware to facilitate performance ofthe functions described herein.

The main memory 306 may be embodied as any type of volatile (e.g.,dynamic random access memory (DRAM), etc.) or non-volatile memory ordata storage capable of performing the functions described herein.Volatile memory may be a storage medium that requires power to maintainthe state of data stored by the medium. Non-limiting examples ofvolatile memory may include various types of random access memory (RAM),such as DRAM or static random access memory (SRAM). One particular typeof DRAM that may be used in a memory module is synchronous dynamicrandom access memory (SDRAM).

In one example, the memory device is a block addressable memory device,such as those based on NAND or NOR technologies. A memory device mayalso include a three-dimensional crosspoint memory device (e.g., Intel3D XPoint™ memory), or other byte addressable write-in-place nonvolatilememory devices. The memory device may refer to the die itself and/or toa packaged memory product. In some examples, 3D crosspoint memory (e.g.,Intel 3D XPoint™ memory) may comprise a transistor-less stackable crosspoint architecture in which memory cells sit at the intersection of wordlines and bit lines and are individually addressable and in which bitstorage is based on a change in bulk resistance. In some examples, allor a portion of the main memory 306 may be integrated into the processor304. The main memory 306 may store various software and data used duringoperation such as one or more applications, data operated on by theapplication(s), libraries, and drivers.

The compute circuitry 302 is communicatively coupled to other componentsof the compute node 300 via the I/O subsystem 308, which may be embodiedas circuitry and/or components to facilitate input/output operationswith the compute circuitry 302 (e.g., with the processor 304 and/or themain memory 306) and other components of the compute circuitry 302. Forexample, the I/O subsystem 308 may be embodied as, or otherwise include,memory controller hubs, input/output control hubs, integrated sensorhubs, firmware devices, communication links (e.g., point-to-point links,bus links, wires, cables, light guides, printed circuit board traces,etc.), and/or other components and subsystems to facilitate theinput/output operations. In some examples, the I/O subsystem 308 mayform a portion of a system-on-a-chip (SoC) and be incorporated, alongwith one or more of the processor 304, the main memory 306, and othercomponents of the compute circuitry 302, into the compute circuitry 302.

The one or more illustrative data storage devices 310 may be embodied asany type of devices configured for short-term or long-term storage ofdata such as, for example, memory devices and circuits, memory cards,hard disk drives, solid-state drives, or other data storage devices.Each data storage device 310 may include a system partition that storesdata and firmware code for the data storage device 310. Each datastorage device 310 may also include one or more operating systempartitions that store data files and executables for operating systemsdepending on, for example, the type of compute node 300.

The communication circuitry subsystem 312 may be embodied as anycommunication circuit, device, or collection thereof, capable ofenabling communications over a network between the compute circuitry 302and another compute device (e.g., an edge gateway node of an edgecomputing system). The communication circuitry subsystem 312 may beconfigured to use any one or more communication technology (e.g., wiredor wireless communications) and associated protocols (e.g., a cellularnetworking protocol such a 3GPP 4G or 5G standard, a wireless local areanetwork protocol such as IEEE 802.11/Wi-Fi®, a wireless wide areanetwork protocol, Ethernet, Bluetooth®, etc.) to effect suchcommunication.

The illustrative communication circuitry subsystem 312 includes anetwork interface controller (NIC) 320, which may also be referred to asa host fabric interface (HFI). The NIC 320 may be embodied as one ormore add-in-boards, daughter cards, network interface cards, controllerchips, chipsets, or other devices that may be used by the compute node300 to connect with another compute device (e.g., an edge gateway node).In some examples, the NIC 320 may be embodied as part of asystem-on-a-chip (SoC) that includes one or more processors, or includedon a multichip package that also contains one or more processors. Insome examples, the NIC 320 may include a local processor (not shown)and/or a local memory (not shown) that are both local to the NIC 320. Insuch examples, the local processor of the NIC 320 may be capable ofperforming one or more of the functions of the compute circuitry 302described herein. Additionally or alternatively, in such examples, thelocal memory of the NIC 320 may be integrated into one or morecomponents of the client compute node at the board level, socket level,chip level, and/or other levels.

Additionally, in some examples, each compute node 300 may include one ormore peripheral devices 314. Such peripheral devices 314 may include anytype of peripheral device found in a compute device or server such asaudio input devices, a display, other input/output devices, interfacedevices, and/or other peripheral devices, depending on the particulartype of the compute node 300. In further examples, the compute node 300may be embodied by a respective edge compute node in an edge computingsystem (e.g., client compute node, edge gateway node, or edgeaggregation node) or like forms of appliances, computers, subsystems,circuitry, or other components.

In a more detailed example, FIG. 3B illustrates a block diagram of anexample of components that may be present in an edge computing node 350for implementing the techniques (e.g., operations, processes, methods,and methodologies) described herein. The edge computing node 350 mayinclude any combinations of the components referenced above, and it mayinclude any device usable with an edge communication network or acombination of such networks. The components may be implemented as ICs,portions thereof, discrete electronic devices, or other modules, logic,hardware, software, firmware, or a combination thereof adapted in theedge computing node 350, or as components otherwise incorporated withina chassis of a larger system.

The edge computing node 350 may include processing circuitry in the formof a processor 352, which may be a microprocessor, a multi-coreprocessor, a multithreaded processor, an ultra-low voltage processor, anembedded processor, or other known processing elements. The processor352 may be a part of a system on a chip (SoC) in which the processor 352and other components are formed into a single integrated circuit, or asingle package, such as the Edison™ or Galileo™ SoC boards from IntelCorporation, Santa Clara, Calif. As an example, the processor 352 mayinclude an Intel® Architecture Core™ based processor, such as a Quark™,an Atom™, an i3, an i5, an i7, an i9, or an MCU-class processor, oranother such processor available from Intel®. However, any number otherprocessors may be used, such as available from Advanced Micro Devices,Inc. (AMD) of Sunnyvale, Calif., a MIPS-based design from MIPSTechnologies, Inc. of Sunnyvale, Calif., an ARM-based design licensedfrom ARM Holdings, Ltd. or a customer thereof, or their licensees oradopters. The processors may include units such as an A5-A12 processorfrom Apple® Inc., a Snapdragon™ processor from Qualcomm® Technologies,Inc., or an OMAP™ processor from Texas Instruments, Inc.

The processor 352 may communicate with a system memory 354 over aninterconnect 356 (e.g., a bus). Any number of memory devices may be usedto provide for a given amount of system memory. As examples, the memorymay be random access memory (RAM) in accordance with a Joint ElectronDevices Engineering Council (JEDEC) design such as the DDR or mobile DDRstandards (e.g., LPDDR, LPDDR2, LPDDR3, or LPDDR4). In particularexamples, a memory component may comply with a DRAM standard promulgatedby JEDEC, such as JESD79F for DDR SDRAM, JESD79-2F for DDR2 SDRAM,JESD79-3F for DDR3 SDRAM, JESD79-4A for DDR4 SDRAM, JESD209 for LowPower DDR (LPDDR), JESD209-2 for LPDDR2, JESD209-3 for LPDDR3, andJESD209-4 for LPDDR4. Such standards (and similar standards) may be,referred to as DDR-based standards and communication interfaces of thestorage devices that implement such standards may be referred to asDDR-based interfaces. In various implementations, the individual memorydevices may be of any number of different package types such as singledie package (SDP), dual die package (DDP) or quad die package (Q17P).These devices, in some examples, may be directly soldered onto amotherboard to provide a lower profile solution, while in other examplesthe devices are configured as one or more memory modules that in turncouple to the motherboard by a given connector. Any number of othermemory implementations may be used, such as other types of memorymodules, e.g., dual inline memory modules (DIMMs) of different varietiesincluding but not limited to microDIMMs or MiniDIMMs.

To provide for persistent storage of information such as data,applications, operating systems and so forth, a storage 358 may alsocouple to the processor 352 via the interconnect 356. In an example, thestorage 358 may be implemented via a solid-state disk drive (SSDD).Other devices that may be used for the storage 358 include flash memorycards, such as SD cards, microSD cards, XD picture cards, and the like,and USB flash drives. In an example, the memory device may be or mayinclude memory devices that use chalcogenide glass, multi-thresholdlevel NAND flash memory, NOR flash memory, single or multi-level PhaseChange Memory (PCM), a resistive memory, nanowire memory, ferroelectrictransistor random access memory (FeTRAM), anti-ferroelectric memory,magnetoresistive random access memory (MRAM) memory that incorporatesmemristor technology, resistive memory including the metal oxide base,the oxygen vacancy base and the conductive bridge Random Access Memory(CB-RAM), or spin transfer torque (STT)-MRAM, a spintronic magneticjunction memory based device, a magnetic tunneling junction (MTJ) baseddevice, a DW (Domain Wall) and SOT (Spin Orbit Transfer) based device, athyristor based memory device, or a combination of any of the above, orother memory.

In low power implementations, the storage 358 may be on-die memory orregisters associated with the processor 352. However, in some examples,the storage 358 may be implemented using a micro hard disk drive (HDD),Further, any number of new technologies may be used for the storage 358in addition to, or instead of, the technologies described, suchresistance change memories, phase change memories, holographic memories,or chemical memories, among others.

The components may communicate over the interconnect 356. Theinterconnect 356 may include any number of technologies, includingindustry standard architecture (ISA), extended ISA (EISA), peripheralcomponent interconnect (PCI), peripheral component interconnect extended(PCIx), PCI express (PCIe), or any number of other technologies. Theinterconnect 356 may be a proprietary bus, for example, used in an SoCbased system. Other bus systems may be included, such as an I2Cinterface, an SPI interface, point to point interfaces, and a power bus,among others.

The interconnect 356 may couple the processor 352 to a transceiver 366,for communications with the connected edge devices 362. The transceiver366 may use any number of frequencies and protocols, such as 2.4Gigahertz (GHz) transmissions under the IEEE 802.15.4 standard, usingthe Bluetooth® low energy (BLE) standard, as defined by the Bluetooth®Special Interest Group, or the ZigBee® standard, among others. Anynumber of radios, configured for a particular wireless communicationprotocol, may be used for the connections to the connected edge devices362. For example, a wireless local area network (WLAN) unit may be usedto implement Wi-Fi® communications in accordance with the Institute ofElectrical and Electronics Engineers (IEEE) 802.11 standard. Inaddition, wireless wide area communications, e.g., according to acellular or other wireless wide area protocol, may occur via a wirelesswide area network (WWAN) unit.

The wireless network transceiver 366 (or multiple transceivers) maycommunicate using multiple standards or radios for communications at adifferent range. For example, the edge computing node 350 maycommunicate with close devices, e.g., within about 10 meters, using alocal transceiver based on BLE, or another low power radio, to savepower. More distant connected edge devices 362, e.g., within about 50meters, may be reached over ZigBee or other intermediate power radios.Both communications techniques may take place over a single radio atdifferent power levels or may take place over separate transceivers, forexample, a local transceiver using BLE and a separate mesh transceiverusing ZigBee.

A wireless network transceiver 366 (e.g., a radio transceiver) may beincluded to communicate with devices or services in the edge cloud 390via local or wide area network protocols. The wireless networktransceiver 366 may be an LPWA transceiver that follows the IEEE802.15.4, or IEEE 802.15.4g standards, among others. The edge computingnode 350 may communicate over a wide area using LoRaWAN™ (Long RangeWide Area Network) developed by Semtech and the LoRa Alliance. Thetechniques described herein are not limited to these technologies butmay be used with any number of other cloud transceivers that implementlong range, low bandwidth communications, such as Sigfox, and othertechnologies. Further, other communications techniques, such astime-slotted channel hopping, described in the IEEE 802.15.4especification may be used.

Any number of other radio communications and protocols may be used inaddition to the systems mentioned for the wireless network transceiver366, as described herein. For example, the transceiver 366 may include acellular transceiver that uses spread spectrum (SPA/SAS) communicationsfor implementing high-speed communications. Further, any number of otherprotocols may be used, such as Wi-Fi® networks for medium speedcommunications and provision of network communications. The transceiver366 may include radios that are compatible with any number of 3GPP(Third Generation Partnership Project) specifications, such as Long TermEvolution (LTE) and 5th Generation (5G) communication systems, discussedin further detail at the end of the present disclosure. A networkinterface controller (NIC) 368 may be included to provide a wiredcommunication to nodes of the edge cloud 390 or to other devices, suchas the connected edge devices 362 (e.g., operating in a mesh). The wiredcommunication may provide an Ethernet connection or may be based onother types of networks, such as Controller Area Network (CAN), LocalInterconnect Network (LIN), DeviceNet, ControlNet, Data Highway+,PROFIBUS, or PROFINET, among many others. An additional NIC 368 may beincluded to enable connecting to a second network, for example, a firstNIC 368 providing communications to the cloud over Ethernet, and asecond NIC 368 providing communications to other devices over anothertype of network.

Any of the radio links described herein may operate according to any oneor more of the following radio communication technologies and/orstandards including but not limited to: a Global System for MobileCommunications (GSM) radio communication technology, a General PacketRadio Service (GPRS) radio communication technology, an Enhanced DataRates for GSM Evolution (EDGE) radio communication technology, and/or aThird Generation Partnership Project (3GPP) radio communicationtechnology, for example Universal Mobile Telecommunications System(UMTS), Freedom of Multimedia Access (FOMA), 3GPP Long Term Evolution(LTE), 3GPP Long Term Evolution Advanced (LIE Advanced), Code divisionmultiple access 2000 (CDMA2000), Cellular Digital Packet Data (CDPD),Mobitex, Third Generation (3G), Circuit Switched Data (CSD), High-SpeedCircuit-Switched Data (HSCSD), Universal Mobile TelecommunicationsSystem (Third Generation) (UMTS (3G)), Wideband Code Division MultipleAccess (Universal Mobile Telecommunications System) (W-CDMA (UMTS)),High Speed Packet Access (HSPA), High-Speed Downlink Packet Access(HSDPA), High-Speed Uplink Packet Access (HSUPA), High Speed PacketAccess Plus (HSPA+), Universal Mobile TelecommunicationsSystem-Time-Division Duplex (UMTS-TDD), Time Division-Code DivisionMultiple Access (TD-CDMA), Time Division-Synchronous Code DivisionMultiple Access (TD-CDMA), 3rd Generation Partnership Project Release 8(Pre-4th Generation) (3GPP Rel. 8 (Pre-4G)), 3GPP Rel. 9 (3rd GenerationPartnership Project Release 9), 3GPP Rel. 10 (3rd Generation PartnershipProject Release 10), 3GPP Rel. 11 (3rd Generation Partnership ProjectRelease 11), 3GPP Rel. 12 (3rd Generation Partnership Project Release12), 3GPP Rel. 13 (3rd Generation Partnership Project Release 13), 3GPPRel. 14 (3rd Generation Partnership Project Release 14), 3GPP Rel. 15(3rd Generation Partnership Project Release 15), 3GPP Rel. 16 (3rdGeneration Partnership Project Release 16), 3GPP Rel. 17 (3rd GenerationPartnership Project Release 17) and subsequent Releases (such as Rel.18, Rel. 19, etc.), 3GPP 5G, 5G, 5G New Radio (5G NR), 3GPP 5G NewRadio, 3GPP LTE Extra, LTE-Advanced Pro, LTE Licensed-Assisted Access(LAA), MuLTEfire, UMTS Terrestrial Radio Access (UTRA), Evolved UMTSTerrestrial Radio Access (E-UTRA), Long Term Evolution Advanced (4thGeneration) (LTE Advanced (4G)), cdmaOne (2G), Code division multipleaccess 2000 (Third generation) (CDMA2000 (3G)), Evolution-Data Optimizedor Evolution-Data Only (EV-DO), Advanced Mobile Phone System (1stGeneration) (AMPS (1G)), Total Access Communication System/ExtendedTotal Access Communication System (TACS/ETACS), Digital AMPS (2ndGeneration) (D-AMPS (2G)), Push-to-talk (PTT), Mobile Telephone System(MTS), Improved Mobile Telephone System (IMIS), Advanced MobileTelephone System (AMTS), OLT (Norwegian for Offentlig LandmobilTelefoni, Public Land Mobile Telephony), MID (Swedish abbreviation forMobiltelefonisystem D, or Mobile telephony system D), Public AutomatedLand Mobile (Autotel/PALM), ARP (Finnish for Autoradiopuhelin, “carradio phone”), NMT (Nordic Mobile Telephony), High capacity version ofNTT (Nippon Telegraph and Telephone) (Hicap), Cellular Digital PacketData (CDPD), Mobitex, DataTAC, Integrated Digital Enhanced Network(iDEN), Personal Digital Cellular (PDC), Circuit Switched Data (CSD),Personal Handy-phone System (PHS), Wideband Integrated Digital EnhancedNetwork (WiDEN), iBurst, Unlicensed Mobile Access (UMA), also referredto as also referred to as 3GPP Generic Access Network, or GAN standard),Zigbee, Bluetooth(r), Wireless Gigabit Alliance (WiGig) standard, mmWavestandards in general (wireless systems operating at 10-300 GHz and abovesuch as WiGig, IEEE 802.11 ad, IEEE 802.11ay, etc.), technologiesoperating above 300 GHz and THz bands, (3GPP/LTE, based or IEEE 802.11pand other) Vehicle-to-Vehicle (V2V) and Vehicle-to-X (V2X) andVehicle-to-Infrastructure (V2I) and Infrastructure-to-Vehicle (I2V)communication technologies, 3GPP cellular V2X, DSRC (Dedicated ShortRange Communications) communication systems such asIntelligent-Transport-Systems and others (typically operating in 5850MHz to 5925 MHz or above (typically up to 5935 MHz following changeproposals in CEPT Report 71)), the European ITS-G5 system (i.e. theEuropean flavor of IEEE 802.11p based DSRC, including ITS-G5A (i.e.Operation of ITS-G5 in European ITS frequency bands dedicated to ITS forsafety re-laced applications in the frequency range 5,875 GHz to 5,905GHz), ITS-G5B (i.e., Operation in European ITS frequency bands dedicatedto ITS non-safety applications in the frequency range 5,855 GHz to 5,875GHz), ITS-G5C (i.e., Operation of ITS applications in the frequencyrange 5,470 GHz to 5,725 GHz)), DSRC in Japan in the 700 MHz band(including 715 MHz to 725 MHz) etc.

Aspects described herein can be used in the context of any spectrummanagement scheme including dedicated licensed spectrum, unlicensedspectrum, license exempt spectrum, (licensed) shared spectrum (such asLSA=Licensed Shared Access in 2.3-2.4 GHz, 3.4-3.6 GHz, 3.6-3.8 GHz andfurther frequencies and SAS=Spectrum Access System/CBRS=CitizenBroadband Radio System in 3.55-3.7 GHz and further frequencies).Applicable spectrum bands include IMT (International MobileTelecommunications) spectrum as well as other types of spectrum/bands,such as bands with national allocation (including 450-470 MHz, 902-928MHz (e.g., allocated for example in US (FCC Part 15)), 863-868.6 MHz(note: allocated for example in European Union (ETSI EN 300 220)),915.9-929.7 MHz (e.g., allocated for example in Japan), 917-923.5 MHz(e.g., allocated for example in South Korea), 755-779 MHz and 779-787MHz (e.g., allocated for example in China), 790-960 MHz, 1710-2025 MHz,2110-2200 MHz, 2300-2400 MHz, 2.4-24835 GHz (e.g., an ISM band withglobal availability, which may be used by Wi-Fi technology family(11b/g/n/ax) or by Bluetooth), 2500-2690 MHz, 698-790 MHz, 610-790 MHz,3400-3600 MHz, 3400-3800 MHz, 3.55-3.7 GHz (e.g., allocated for examplein the US for Citizen Broadband Radio Service), 5.15-5.25 GHz and5.25-5.35 GHz and 5.47-5.725 GHz and 5.725-5.85 GHz bands (e.g.,allocated for example in the US (FCC part 15), including four U-NIIbands in total 500 MHz spectrum), 5.725-5.875 GHz (e.g., allocated forexample in EU (ETSI EN 301 893)), 5.47-565 GHz (e.g., allocated forexample in South Korea, 5925-7125 MHz and 5925-6425 MHz band (e.g., inUS and EU, respectively, where a Wi-Fi system may include the 6 GHzspectrum as operating band), IMT-advanced spectrum, IMT-2020 spectrum(e.g., including 3600-3800 MHz, 3.5 GHz bands, 700 MHz bands, bandswithin the 24.25-86 GHz range, etc.), spectrum made available underFCC's “Spectrum frontier” 5G initiative (including 27.5-28.35 GHz,29.1-29.25 GHz, 31-31.3 GHz, 37-38.6 GHz, 38.6-40 GHz, 42-42.5 GHz,57-64 GHz, 71-76 GHz, 81-86 GHz and 92-94 GHz, etc), the ITS(Intelligent Transport Systems) band of 5.9 GHz (typically 5.85-5.925GHz) and 63-64 GHz, bands currently allocated to WiGig such as WiGigBand 1 (5724-59.40 GHz), WiGig Band 2 (59.40-61.56 GHz) and WiGig Band 3(61.56-63.72 GHz) and WiGig Band 4 (63.72-65.88 GHz), 57-64/66 GHz(e.g., this band may have near-global designation for Multi-GigabitWireless Systems (MGWS)/WiGig). In US (FCC part 15) allocates total 14GHz spectrum, while EU (ETSI EN 302 567 and ETSI EN 301 217-2 for fixedP2P) allocates total 9 GHz spectrum), the 70.2 GHz-71 GHz band, any bandbetween 65.88 GHz and 71 GHz, bands currently allocated to automotiveradar applications such as 76-81 GHz, or bands including 94-300 GHz andabove. Furthermore, the scheme can be used on a secondary basis on bandssuch as the TV White Space bands (typically below 790 MHz) where inparticular the 400 MHz and 700 MHz bands are promising candidates.Besides cellular applications, specific applications for verticalmarkets may be addressed such as PMSE (Program Making and SpecialEvents), medical, health, surgery, automotive, low-latency, drones, etc.applications.

Aspects described herein can also implement a hierarchical applicationof the scheme is possible, e.g. by introducing a hierarchicalprioritization of usage for different types of users (e.g.,low/medium/high priority, etc.), based on a prioritized access to thespectrum e.g. with highest priority to tier-1 users, followed by tier-2,then tier-3, etc. users, etc.

Aspects described herein can also be applied to different Single Carrieror OFDM flavors (CP-OFDM, SC-TDMA, SC-OFDM, filter bank-basedmulticarrier (FBMC), OFDMA, etc.) and in particular 3GPP NR (New Radio)by allocating the OFDM carrier data bit vectors to the correspondingsymbol resources.

Some of the features in this document are defined for the network side,such as Access Points, eNodeBs, New Radio (NR) or next generation NodeBs (gNodeB or gNB—in an example, this term may be used in the context of3GPP fifth generation (5G) communication systems), etc. Still, a UserEquipment (UE) may take this role as well and act as an Access Points,eNodeBs, gNodeBs, etc. I.e., some or all features defined for networkequipment may be implemented by a UE.

Given the variety of types of applicable communications from the deviceto another component or network, applicable communications circuitryused by the device may include or be embodied by any one or more ofcomponents 364, 366, 368, or 370. Accordingly, in various examples,applicable means for communicating (e.g., receiving, transmitting, etc.)may be embodied by such communications circuitry.

The edge computing node 350 may include or be coupled to accelerationcircuitry 364, which may be embodied by one or more AI accelerators, aneural compute stick, neuromorphic hardware, an FPGA, an arrangement ofGPUs, one or more SoCs, one or more CPUs, one or more digital signalprocessors, dedicated ASICs, or other forms of specialized processors orcircuitry designed to accomplish one or more specialized tasks. Thesetasks may include AI processing (including machine learning, training,inferencing, and classification operations), visual data processing,network data processing, object detection, rule analysis, or the like.Accordingly, in various examples, applicable means for acceleration maybe embodied by such acceleration circuitry.

The interconnect 356 may couple the processor 352 to a sensor hub orexternal interface 370 that is used to connect additional devices orsubsystems. The devices may include sensors 372, such as accelerometers,level sensors, flow sensors, optical light sensors, camera sensors,temperature sensors, a global positioning system (GPS) sensors, pressuresensors, barometric pressure sensors, and the like. The hub or interface370 further may be used to connect the edge computing node 350 toactuators 374, such as power switches, valve actuators, an audible soundgenerator, a visual warning device, and the like.

In some optional examples, various input/output (I/O) devices may bepresent within or connected to, the edge computing node 350. Forexample, a display or other output device 384 may be included to showinformation, such as sensor readings or actuator position. An inputdevice 386, such as a touch screen or keypad may be included to acceptinput. An output device 384 may include any number of forms of audio orvisual display, including simple visual outputs such as binary statusindicators (e.g., LEDs) and multi-character visual outputs, or morecomplex outputs such as display screens (e.g., LCD screens), with theoutput of characters, graphics, multimedia objects, and the like beinggenerated or produced from the operation of the edge computing node 350.

A battery 376 may power the edge computing node 350, although, inexamples in which the edge computing node 350 is mounted in a fixedlocation, it may have a power supply coupled to an electrical grid. Thebattery 376 may be a lithium ion battery, or a metal-air battery, suchas a zinc-air battery, an aluminum-air battery, a lithium-air battery,and the like.

A battery monitor/charger 378 may be included in the edge computing node350 to track the state of charge (SoCh) of the battery 376. The batterymonitor/charger 378 may be used to monitor other parameters of thebattery 376 to provide failure predictions, such as the state of health(SoH) and the state of function (SoF) of the battery 376. The batterymonitor/charger 378 may include a battery monitoring integrated circuit,such as an LTC4020 or an LTC2990 from Linear Technologies, an ADT7488Afrom ON Semiconductor of Phoenix Ariz., or an IC from the UCD90xxxfamily from Texas Instruments of Dallas, Tex. The batterymonitor/charger 378 may communicate the information on the battery 376to the processor 352 over the interconnect 356. The batterymonitor/charger 378 may also include an analog-to-digital (ADC)converter that enables the processor 352 to directly monitor the voltageof the battery 376 or the current flow from the battery 376. The batteryparameters may be used to determine actions that the edge computing node350 may perform, such as transmission frequency, mesh network operation,sensing frequency, and the like.

A power block 380, or other power supply coupled to a grid, may becoupled with the battery monitor/charger 378 to charge the battery 376.In some examples, the power block 380 may be replaced with a wirelesspower receiver to obtain the power wirelessly, for example, through aloop antenna in the edge computing node 350. A wireless battery chargingcircuit, such as an LTC4020 chip from Linear Technologies of Milpitas,Calif., among others, may be included in the battery monitor/charger378. The specific charging circuits may be selected based on the size ofthe battery 376, and thus, the current required. The charging may beperformed using the Airfuel standard promulgated by the AirfuelAlliance, the Qi wireless charging standard promulgated by the WirelessPower Consortium, or the Rezence charging standard, promulgated by theAlliance for Wireless Power, among others.

The storage 358 may include instructions 382 in the form of software,firmware, or hardware commands to implement the techniques describedherein. Although such instructions 382 are shown as code blocks includedin the memory 354 and the storage 358, it may be understood that any ofthe code blocks may be replaced with hardwired circuits, for example,built into an application specific integrated circuit (ASIC).

In an example, the instructions 382 provided via the memory 354, thestorage 358, or the processor 352 may be embodied as a non-transitory,machine-readable medium 360 including code to direct the processor 352to perform electronic operations in the edge computing node 350. Theprocessor 352 may access the non-transitory, machine-readable medium 360over the interconnect 356. For instance, the non-transitory,machine-readable medium 360 may be embodied by devices described for thestorage 358 or may include specific storage units such as optical disks,flash drives, or any number of other hardware devices. Thenon-transitory, machine-readable medium 360 may include instructions todirect the processor 352 to perform a specific sequence or flow ofactions, for example, as described with respect to the flowchart(s) andblock diagram(s) of operations and functionality depicted above. As usedin, the terms “machine-readable medium” and “computer-readable medium”are interchangeable.

In further examples, a machine-readable medium also includes anytangible medium that is capable of storing, encoding or carryinginstructions for execution by a machine and that cause the machine toperform any one or more of the methodologies of the present disclosureor that is capable of storing, encoding or carrying data structuresutilized by or associated with such instructions. A “machine-readablemedium” thus may include but is not limited to, solid-state memories,and optical and magnetic media. Specific examples of machine-readablemedia include non-volatile memory, including but not limited to, by wayof example, semiconductor memory devices (e.g., electricallyprogrammable read-only memory (EPROM), electrically erasableprogrammable read-only memory (EEPROM)) and flash memory devices;magnetic disks such as internal hard disks and removable disks;magneto-optical disks; and CD-ROM and DVD-ROM disks. The instructionsembodied by a machine-readable medium may further be transmitted orreceived over a communications network using a transmission medium via anetwork interface device utilizing any one of a number of transferprotocols (e.g., HTTP).

A machine-readable medium may be provided by a storage device or otherapparatus which is capable of hosting data in a non-transitory format.In an example, information stored or otherwise provided on amachine-readable medium may be representative of instructions, such asinstructions themselves or a format from which the instructions may bederived. This format from which the instructions may be derived mayinclude source code, encoded instructions (e.g., in compressed orencrypted form), packaged instructions (e.g., split into multiplepackages), or the like. The information representative of theinstructions in the machine-readable medium may be processed byprocessing circuitry into the instructions to implement any of theoperations discussed herein. For example, deriving the instructions fromthe information (e.g., processing by the processing circuitry) mayinclude: compiling (e.g., from source code, object code, etc.),interpreting, loading, organizing (e.g., dynamically or staticallylinking), encoding, decoding, encrypting, unencrypting, packaging,unpackaging, or otherwise manipulating the information into theinstructions.

In an example, the derivation of the instructions may include assembly,compilation, or interpretation of the information (e.g., by theprocessing circuitry) to create the instructions from some intermediateor preprocessed format provided by the machine-readable medium. Theinformation, when provided in multiple parts, may be combined, unpacked,and modified to create the instructions. For example, the informationmay be in multiple compressed source code packages (or object code, orbinary executable code, etc.) on one or several remote servers. Thesource code packages may be encrypted when in transit over a network anddecrypted, uncompressed, assembled (e.g., linked) if necessary, andcompiled or interpreted (e.g., into a library, stand-alone executable,etc.) at a local machine, and executed by the local machine.

Each of the block diagrams of FIGS. 3A and 3B are intended to depict ahigh-level view of components of a device, subsystem, or arrangement ofan edge computing node. However, it will be understood that some of thecomponents shown may be omitted, additional components may be present,and a different arrangement of the components shown may occur in otherimplementations.

FIG. 4 illustrates example components of an autonomous driving system400, according to an example. The autonomous driving system 400 includescategories that are illustrated for convenience, but may includeoverlapping components, different subcomponents not illustrated, or mayswap components with other categories. The components of the autonomousdriving system 400 include a Perception component, a Decision andControl component (e.g., operating with information in a worldcoordinate space), and a Vehicle Platform Manipulation component (e.g.,operating with information in a vehicle coordinate space). ThePerception component may include sensing, sensor fusion, localization,semantic understanding, a world model, or other perception aspects usedto operate an autonomous vehicle. The Decision and Control component mayinclude trajectory generation, energy management, diagnosis and faultmanagement, reactive control, vehicle platform abstraction, or otherworld coordinate system aspects used to operate an autonomous vehicle.The Vehicle Platform Manipulation component may include platformstabilization, passive safety, trajectory execution includingpropulsion, steering, or braking, or the like.

For the groups of Perception and Decision and Control, differentapproaches may be applied based on whether how many of the componentsare internal to the AV, for example all, some, or many, and how many areexternal.

In one extreme all the perception done for the AV is based on sensorsand other information passively obtained by the vehicle, and alldecision and control are performed by processing units, including AIs,inside of the vehicle (e.g., an entirely internal structure). None orvery little wireless communications, as known as V2X(Vehicle-to-Everything) is deployed in this example. For example, thepure reception and broadcast transmission of Basic Safety Messages (BSM)or Cooperative Awareness Messages (CAM) may be deployed, and theinformation contained is processed by the system similar to anadditional passive sensor.

On the other extreme, all perception may occur outside of the vehicle,(e.g., the road infrastructure is equipped with multiple sensors, AVshave fully dependable connection to a wireless network, AVs have verylittle processing capabilities, and the decision and control happensfully outside of the vehicle in a remote-control set-up). In thisextreme, as mentioned, a dependable connection is relied on to receivethe perception for determining decision and control (or the decision andcontrol may occur outside the vehicle in some examples).

In practice, a realistic approach frequently seen is an implementationbetween those two extremes. On one hand, it is useful to have an AV thatis able to operate when there is no wireless network or connectionavailable, and on the other hand, cooperation between an AV and supportby data processing and consolidation centers in the cloud and the edge,allows for an optimal utilization of computational resources and moreefficient traffic operation. In a hybrid approach, a maximum level ofsafety for an AV may be achieved.

The use of AI techniques to solve the problem of control and managementof AVs may follow the principles of the control loop model“observe-orient-decide-act”, ETSI ENI has developed a framework based inthis model for future deployment and operation of mobile networks. Theobjective there is to automate the complex human-dependentdecision-making process. The present systems and methods are used toguide transition from manual to automated driving. FIG. 5 introduces thesystem architecture to support the AI framework defined by ETSI ENI.ETSI ENI focuses on improving the network operator experience, addingclosed-loop AI mechanisms based on context-aware, metadata-drivenpolicies to more quickly recognize and incorporate new and changedknowledge, and make actionable decisions.

The components or subsystems above may involve combinations of hardwareand software, and many of these components will fall into the scope ofeither or both the RED or the Cybersecurity act in Europe. Inparticular, the software ruining in those components or subsystems maybe able to be updated over-the-air (OTA) in the future.

Some or all of the components or subsystems above may be used with thesystems and techniques disclosed herein. For example, an external entity(validated or authenticated, in some examples) may request taginformation related to which requirements of RED are met and whichrequirements of the Cybersecurity Act are met. An external entity mayrequest tag information related to whether a component or subsystem isallowed to be operated in a specific region (including US, Europe, Asia)or in specific countries. An external entity may deactivate specificcomponents or subsystems when operation is not authorized in a specificregion (including US, Europe, Asia) or in specific countries, or thelike.

FIG. 5 illustrates an example networked intelligence architecture 500,according to an example. The networked intelligence architecture 500 mayinclude or implement an ETSI ENI Architecture (e.g., ETSI GS ENI-005ENI, System Architecture, of Release 1). In an example, the ENI systemcomponents may be operated at the edge, distributed, or in the cloud.

In an example, the internal and external components of an AV have aninteraction with the ENI System components as shown in FIGS. 6-8 below.Perception functional components may be associated to the KnowledgeManagement, Context Awareness, and Situational Awareness components ofthe networked intelligence architecture 500. Decision and Controlfunctional components may be associated to Cognition Management,Model-Driven Engineering, and Policy Management components of thenetworked intelligence architecture 500. Vehicle Platform Manipulationfunctional components may be associated to Denormalization and OutputGeneration components of the networked intelligence architecture 500.

Dashed lines FIG. 5 (e.g., line 510) represent communication paths thatare not defined by ENI. Solid lines in FIG. 5 (e.g., line 520) representexternal reference paint connections defined by ENI. Dotted lines inFIG. 5 (e.g., line 530) represent internal reference point connectionsdefined by ENI.

FIG. 6 illustrates an example perception component connection diagram600, according to an example. The perception component connectiondiagram 600 shows interactions between the Perception component and theKnowledge Management, Context Awareness, or Situational Awarenesscomponents (e.g., of FIG. 5 ). Interactions may include registration,authentication, information requests such as to support processing ofdata, knowledge updates, de-registration, link termination, or the like.The Perception component may include cooperative perception with V2X orV2V, such as for understanding context.

FIG. 7 illustrates an example decision and control component connectiondiagram 700, according to an example. The decision and control componentconnection diagram 700 shows interactions between the Decision andControl component and the Cognition Management, Model-DrivenEngineering, or Policy Management components (e.g., of FIG. 5 ).Interactions may include registration, authentication, informationrequests such as to support decision making, knowledge updates,de-registration, link termination, or the like.

FIG. 8 illustrates an example vehicle platform manipulation componentconnection diagram 800, according to an example. The vehicle platformmanipulation component connection diagram 800 shows interactions betweenthe vehicle platform manipulation component and the denormalization oroutput generation components (e.g., of FIG. 5 ). Interactions mayinclude registration, authentication, providing information such asdecisions available, requesting information such as de-normalization oroutput creation, de-registration, link termination, or the like.

FIG. 9 illustrates an example component tagging connection diagram 900,according to an example. Example mechanisms and information betweenexternal entities and an autonomous vehicle are outlined in FIG. 9 . Anexternal entity, such as a government administration, a manufacturer(which may act as an intermediary), an assembler, etc., may requestcomponent security information. Requesting security information mayinclude registration or authentication, a request for a tag, a replywith a tag (which may include use of an intermediary), confirmation ofconformity of a component or system, sending information from a tag orsending the tag, or the like. The tag may be verified for issuer, form,or authenticity.

The external entity may request information on conformity of aparticular vehicle or particular set of vehicles to a specificregulation (or regulatory framework, such as compliance with allregulations for a region, country, etc.). The external entity may firstbe registered, authenticated, or validated as being authorized to accessthe information. After this step, the external entity may send therequest for tagging information. The tagging information may be sent ina response from the component or subsystem of an AV to the externalentity. When compliance is not fulfilled (e.g., the tag or responseindicates that a particular regulation or regulatory framework is notsatisfied by the component or subsystem), the external entity may takefurther action. For example, the external entity may requestdeactivation of any non-compliant component or subsystem at the AV. TheAV may return a response to the request with confirmation of thedeactivation.

FIG. 10 illustrates an example tagging data structure 1000, according toan example. There are different possibilities for the structure of thetag. It may include an identification of the component or subsystem, thelist of requirements and a check whether each requirement is fulfilledor not. The tagging data structure 1000 may include a taggingidentifier, a tagging authority (e.g., manufacturer), a taggingconfirmation code (e.g., an encryption key), public tagging informationthat may be shared on request, or private tagging information that maybe shared when a request is authorized, for example.

In response to a request for regulatory compliance information, thetagging data structure 1000 may be accessed (e.g., by a controller orprocessor of vehicle) for a particular component or subsystem. In someexamples, the tagging data structure 1000 may be used to store data forall components and subsystems of a vehicle. In another example, eachcomponent or subsystem or a set of components or subsystems may havetheir own tagging data structure 1000. The tagging data structure 1000may be stored locally at the vehicle (e.g., in long term storage of thevehicle) or in the cloud (e.g., accessible to the vehicle, but remotelystored). Before replying to a request for a tag, the vehicle mayauthenticate, register, or validate the requesting entity. In someexamples, different information may be available to different entities.For example, a first level of authorization may allow an entity accessto the public tagging information of the tagging data structure 1000,while a second level of authorization may allow an entity access to theprivate tagging information of the tagging data structure 1000. Thetagging data structure 1000 may be specific to a component or subsystem,for example with a unique identifier in some examples.

In an example, in response to a request, the tagging data structure 1000may be sent as a tag to a requesting entity. In other examples, onlyportions of the tagging data structure 1000 or information from thetagging data structure 1000 may be sent (e.g., as a tag, in a responsemessage, etc.). Information from the tag may be shared directly or maybe altered before sending. For example, in some cases the raw data fromthe tagging data structure 1000 may be sent, while in other cases, moregeneral information such as “complaint” or “non-compliant” may be sent.In some examples, information such as a level of security compliance maybe sent (e.g., low, medium, or high) indicating compliance. A highestlevel of compliance may be used for components or subsystems that havebeen verified (e.g., by a compliance user) to comply with a relevantregulation (optionally within a specified period of time, such as a day,a week, a month, a year, etc.). A medium compliance level may be usedfor a component or subsystem that was verified, but after a particulartime period has elapsed (e.g., because regulations may have changed),such as a month, a year, etc. In an example, a low compliance level maybe used for components or subsystems that are unlikely to be regulated,for components or subsystems where a long time period has passed (e.g.,five years) since verification, for components or subsystems that onlycomply with some regulations but not all, or the like.

FIG. 11 illustrates a flowchart of an example process 1100 forinterfacing a networked intelligence system with vehicle components,according to an example. The process 1100 may be performed by an edgedevice, for example via a processor implementing instructions stored ina memory. The edge device tray operate within an architecture of devicesconforming to a standard from an ETI ENI standards group. The term “edgedevice” may include an “edge base station” or “edge node” (including aMulti-access Edge Computing (MEC) Node, for example) or a communicationdevice in the vehicle that is interacting with such an edge base stationor edge node. An edge base station or edge node may be located at afixed geographic location, (e.g., at the roadside). In an example, avehicle carries such an edge base station or edge node (part of itsfunctionalities or in full)).

The process 1100 includes an operation 1110 to receive a request from avehicle component to register with an artificial intelligence processingcomponent. The artificial intelligence processing component may includea knowledge management component, a content awareness component, acognition management component, a situational awareness component, amodel-driven engineering component, a policy management component, orthe like. The vehicle component may include a perception component, adecision and control component, a platform manipulation component, orthe like.

The process 1100 includes an operation 1120 to send an acknowledgementof the registration to the vehicle component.

The process 1100 includes an operation 1130 to receive a request for aservice of the artificial intelligence processing component. The servicemay include a denormalization service, an output generation service, arequest for information, a request for processing, etc. In an example,the request may include a tag identifying a security compliance level.

The process 1100 includes an operation 1140 to provide, to the vehiclecomponent in response to the request, a response from the service.

The process 1100 may further include receiving a de-registration requestfrom the vehicle component and terminating a link between the vehiclecomponent and the artificial intelligence processing component.

FIG. 12 illustrates a flowchart of an example process 1200 for taggingor providing tagging information related to a security profile ofcomponents of a networked architecture, according to an example. Process1200 may be performed by an edge device. The term “edge device” mayinclude an “edge base station” or “edge node” (including a Multi-accessEdge Computing (MEC) Node, for example) or a communication device in thevehicle that is interacting with such an edge base station or edge node.An edge base station or edge node may be located at a fixed geographiclocation, (e.g., at the roadside). In an example, a vehicle carries suchan edge base station or edge node (part of its functionalities or infull)).

The process 1200 includes an operation 1210 to receive a request for asecurity compliance level of a vehicle component corresponding to driversupport or automated driving control (e.g., a vehicle component). Thesecurity compliance level may include a low level (e.g., not compliant,or only compliant with a minimum requirement), a medium level (e.g.,fully compliant with one regulation but only partially compliant withanother, or compliant in a first jurisdiction but not in a secondjurisdiction), and a highest level of compliance (e.g., fully compliantwith a set of regulations or all regulations for a jurisdiction orworld-wide). In an example, the security compliance level corresponds toArticle 3 requirements of a Radio Equipment Directive regulation of theEuropean Union or a Cybersecurity Act of the European Union. The requestmay be received from a device external to a network including thevehicle component and the edge device. The requesting device may beauthenticated and receive private information or may receive publicinformation.

The process 1200 includes an operation 1220 to retrieve a tag for thevehicle component, the tag identifying the security compliance level. Inan example, the tag has a data structure including a tagging identifier,a tagging authority, a tagging confirmation code, public tagginginformation including the security compliance level, private tagginginformation shared only when the request is authorized, a securityfeature (e.g., a digital signature, a proof of origin, informationenabling protection of its integrity, such as a hash), etc.), or thelike.

The process 1200 includes an operation 1230 to provide, for the vehiclecomponent in response to the request, the security compliance level fromthe tag.

Additional examples of the presently described method, system, anddevice embodiments include the following, non-limiting configurations.Each of the non-limiting examples may stand on its own, or may becombined in any permutation or combination with any one or more of theother examples provided below or throughout the present disclosure.

Example 1 is an edge device configured to support operations of avehicle comprising: processing circuitry; and a memory device comprisinginstructions stored thereon, wherein the instructions, when executed bythe processing circuitry, configure the processing circuitry to performoperations to: receive a request from a vehicle component to registerwith an artificial intelligence processing component of the edge device;send an acknowledgement of the registration to the vehicle component;receive a request for a service of the artificial intelligenceprocessing component; and provide, to the vehicle component in responseto the request, a response from the service.

In Example 2, the subject matter of Example 1 includes, wherein theartificial intelligence processing component is one of a knowledgemanagement component, a content awareness component, a cognitionmanagement component, a situational awareness component, a model-drivenengineering component, or a policy management component.

In Example 3, the subject matter of Examples 1-2 includes, wherein thevehicle component is one of a perception component, a decision andcontrol component, or a platform manipulation component.

In Example 4, the subject matter of Examples 1-3 includes, wherein theservice is a denormalization service or an output generation service.

In Example 5, the subject matter of Examples 1-4 includes, wherein theinstructions further cause the processing circuitry to: receive ade-registration request from the vehicle component; and terminate a linkbetween the vehicle component and the artificial intelligence processingcomponent.

In Example 6, the subject matter of Examples 1-5 includes, wherein theedge device operates within an architecture of devices conforming to astandard from an ETSI ENI standards group.

In Example 7, the subject matter of Examples 1-6 includes, wherein therequest to register includes a tag identifying a security compliancelevel.

Example 8 is at least one machine-readable storage medium comprisinginstructions for operating an edge device configured to supportoperations of a vehicle, wherein the instructions, when executed by aprocessing circuitry of an edge computing device operable in an edgecomputing system, cause the processing circuitry to perform operationsthat: receive a request from a vehicle component to register with anartificial intelligence processing component of the edge device; send anacknowledgement of the registration to the vehicle component; receive arequest for a service of the artificial intelligence processingcomponent; and provide, to the vehicle, component in response to therequest, a response from the service.

In Example 9, the subject matter of Example 8 includes, wherein theartificial intelligence processing component is one of a knowledgemanagement component, a content awareness component, a cognitionmanagement component, a situational awareness component, a model-drivenengineering component, or a policy management component.

In Example 10, the subject matter of Examples 8-9 includes, wherein thevehicle component is one of a perception component, a decision andcontrol component, or a platform manipulation component.

In Example 11, the subject matter of Examples 8-10 includes, wherein theservice is a denormalization service or an output generation service.

In Example 12, the subject matter of Examples 8-11 includes, wherein theinstructions further cause the processing circuitry to: receive ade-registration request from the vehicle component; and terminate a linkbetween the vehicle component and the artificial intelligence processingcomponent.

In Example 13, the subject matter of Examples 8-12 includes, wherein theedge device operates within an architecture of devices conforming to astandard from an ETSI ENI standards group.

In Example 14, the subject matter of Examples 8-13 includes, wherein therequest to register includes a tag identifying a security compliancelevel.

Example 15 is a method performed by a processor of an edge deviceconfigured to support operations of a vehicle, the method comprising:receiving a request from a vehicle component to register with anartificial intelligence processing component of the edge device; sendingan acknowledgement of the registration to the vehicle component;receiving a request for a service of the artificial intelligenceprocessing component; and providing, to the vehicle component inresponse to the request, a response from the service.

In Example 16, the subject matter of Example 15 includes, wherein theartificial intelligence processing component is one of a knowledgemanagement component, a content awareness component, a cognitionmanagement component, a situational awareness component, a model-drivenengineering component, or a policy management component.

In Example 1′7, the subject matter of Examples 15-16 includes, whereinthe vehicle component is one of a perception component, a decision andcontrol component, or a platform manipulation component.

In Example 18, the subject matter of Examples 15-17 includes, whereinthe service is a denormalization service or an output generationservice.

In Example 19, the subject matter of Examples 15-18 includes, receivinga de-registration request from the vehicle component; and terminating alink between the vehicle component and the artificial intelligenceprocessing component.

In Example 20, the subject matter of Examples 15-19 includes, whereinthe edge device operates within an architecture of devices conforming toa standard from an ETSI ENI standards group.

In Example 21, the subject matter of Examples 15-20 includes, whereinthe request to register includes a tag identifying a security compliancelevel.

Example 22 is an edge device configured to support operations of avehicle comprising: processing circuitry; and a memory device comprisinginstructions stored thereon, wherein the instructions, when executed bythe processing circuitry, configure the processing circuitry to performoperations to: receive a request for a security compliance level of avehicle component corresponding to driver support or automated drivingcontrol; retrieve a tag for the vehicle component, the tag identifyingthe security compliance level; and provide, for the vehicle component inresponse to the request, the security compliance level from the tag.

In Example 23, the subject matter of Example 22 includes, wherein thesecurity compliance level includes a low level, a medium, level, and ahighest level of compliance.

In Example 24, the subject matter of Examples 22-23 includes, whereinthe security compliance level corresponds to Article 3 requirements of aRadio Equipment Directive regulation of the European Union.

In Example 25, the subject matter of Examples 22-24 includes, whereinthe security compliance level corresponds to a Cybersecurity Act of theEuropean Union.

In Example 26, the subject matter of Examples 22-25 includes, whereinthe request is from a device on a network that is external to a networkof the vehicle component and the edge device.

In Example 27, the subject matter of Examples 22-26 includes, whereinthe tag has a data structure including a tagging identifier, a taggingauthority, a tagging confirmation code, public tagging informationincluding the security compliance level, private tagging informationshared only when the request is authorized, and a security feature.

Example 28 is at least one machine-readable storage medium comprisinginstructions for operating an edge device configured to supportoperations of a vehicle, wherein the instructions, when executed by aprocessing circuitry of an edge computing device operable in an edgecomputing system, cause the processing circuitry to perform operationsthat: receive a request for a security compliance level of a vehiclecomponent corresponding to driver support or automated driving control;retrieve a tag for the vehicle component, the tag identifying thesecurity compliance level; and provide, for the vehicle component inresponse to the request, the security compliance level from the tag.

In Example 29, the subject matter of Example 28 includes, wherein thesecurity compliance level includes a low level, a medium, level, and ahighest level of compliance.

In Example 30, the subject matter of Examples 28-29 includes, whereinthe security compliance level corresponds to Article 3 requirements of aRadio Equipment Directive regulation of the European Union.

In Example 31, the subject matter of Examples 28-30 includes, whereinthe security compliance level corresponds to a Cybersecurity Act of theEuropean Union.

In Example 32, the subject matter of Examples 28-31 includes, whereinthe request is from a device on a network that is external to a networkof the vehicle component and the edge device.

In Example 33, the subject matter of Examples 28-32 includes, whereinthe tag has a data structure including a tagging identifier, a taggingauthority, a tagging confirmation code, public tagging informationincluding the security compliance level, private tagging informationshared only when the request is authorized, and a security feature.

Example 34 is a method performed by a processor of an edge deviceconfigured to support operations of a vehicle, the method comprising:receiving a request for a security compliance level of a vehiclecomponent corresponding to driver support or automated driving control;retrieving a tag for the vehicle component, the tag identifying thesecurity compliance level; and providing, for the vehicle component inresponse to the request, the security compliance level from the tag.

In Example 35, the subject matter of Example 34 includes, wherein thesecurity compliance level includes a low level, a medium, level, and ahighest level of compliance.

In Example 36, the subject matter of Examples 34-35 includes, whereinthe security compliance level corresponds to Article 3 requirements of aRadio Equipment Directive regulation of the European Union.

In Example 37, the subject matter of Examples 34-36 includes, whereinthe security compliance level corresponds to a Cybersecurity Act of theEuropean Union.

In Example 38, the subject matter of Examples 34-37 includes, whereinthe request is from a device on a network that is external to a networkof the vehicle component and the edge device.

In Example 39, the subject matter of Examples 34-38 includes, whereinthe tag has a data structure including a tagging identifier, a taggingauthority, a tagging confirmation code, public tagging informationincluding the security compliance level, private tagging informationshared only when the request is authorized, and a security feature.

Example 40 is at least one machine-readable medium includinginstructions that, when executed by processing circuitry, cause theprocessing circuitry to perform operations to implement of any ofExamples 1-39.

Example 41 is an apparatus comprising means to implement of any ofExamples 1-39.

Example 42 is a system to implement of any of Examples 1-39.

Example 43 is a method to implement of any of Examples 1-39.

In the above Detailed Description, various features may be groupedtogether to streamline the disclosure. However, claims may not set forthevery feature disclosed herein as embodiments may feature a subset ofsaid features. Further, embodiments may include fewer features thanthose disclosed in a particular example. Thus, the following claims arehereby incorporated into the Detailed Description, with a claim standingon its own as a separate embodiment.

1.-43. (canceled)
 44. An edge device configured to support operations of a vehicle comprising: processing circuitry; and a memory device comprising instructions stored thereon, wherein the instructions, when executed by the processing circuitry, configure the processing circuitry to perform operations to: receive a request for a security compliance level of a vehicle component corresponding to driver support or automated driving control; retrieve a tag for the vehicle component, the tag identifying the security compliance level; and provide, for the vehicle component in response to the request, the security compliance level from the tag.
 45. The edge device of claim 44, wherein the security compliance level includes a low level, a medium, level, and a highest level of compliance.
 46. The edge device of claim 44, wherein the security compliance level corresponds to Article 3 requirements of a Radio Equipment Directive regulation of the European Union.
 47. The edge device of claim 44, wherein the security compliance level corresponds to a Cybersecurity Act of the European Union.
 48. The edge device of claim 44, wherein the request is from a device on a network that is external to a network of the vehicle component and the edge device.
 49. The edge device of claim 44, wherein the tag has a data structure including a tagging identifier, a tagging authority, a tagging confirmation code, public tagging information including the security compliance level, private tagging information shared only when the request is authorized, and a security feature.
 50. At least one machine-readable storage medium comprising instructions for operating an edge device configured to support operations of a vehicle, wherein the instructions, when executed by a processing circuitry of an edge computing device operable in an edge computing system, cause the processing circuitry to perform operations that: receive a request for a security compliance level of a vehicle component corresponding to driver support or automated driving control; retrieve a tag for the vehicle component, the tag identifying the security compliance level; and provide, for the vehicle component in response to the request, the security compliance level from the tag.
 51. The machine-readable storage medium of claim 50, wherein the security compliance level includes a low level, a medium, level, and a highest level of compliance.
 52. The machine-readable storage medium of claim 50, wherein the security compliance level corresponds to Article 3 requirements of a Radio Equipment Directive regulation of the European Union.
 53. The machine-readable storage medium of claim 50, wherein the security compliance level corresponds to a Cybersecurity Act of the European Union.
 54. The machine-readable storage medium of claim 50, wherein the request is from a device on a network that is external to a network of the vehicle component and the edge device.
 55. The machine-readable storage medium of claim 50, wherein the tag has a data structure including a tagging identifier, a tagging authority, a tagging confirmation code, public tagging information including the security compliance level, private tagging information shared only when the request is authorized, and a security feature.
 56. An edge device configured to support operations of a vehicle comprising: processing circuitry; and a memory device comprising instructions stored thereon, wherein the instructions, when executed by the processing circuitry, configure the processing circuitry to perform operations to: receive a request from a vehicle component to register with an artificial intelligence processing component of the edge device; send an acknowledgement of the registration to the vehicle component; receive a request for a service of the artificial intelligence processing component; and provide, to the vehicle component in response to the request, a response from the service.
 57. The edge device of claim 56, wherein the artificial intelligence processing component is one of a knowledge management component, a content awareness component, a cognition management component, a situational awareness component, a model-driven engineering component, or a policy management component.
 58. The edge device of claim 56, wherein the vehicle component is one of a perception component, a decision and control component, or a platform manipulation component.
 59. The edge device of claim 56, wherein the service is a denormalization service or an output generation service.
 60. The edge device of claim 56, wherein the instructions further cause the processing circuitry to: receive a de-registration request from the vehicle component; and terminate a link between the vehicle component and the artificial intelligence processing component.
 61. The edge device of claim 56, wherein the edge device operates within an architecture of devices conforming to a standard from a European Telecommunications Standards Institute (ETSI) Experiential Networked Intelligence (ENI) standards group.
 62. The edge device of claim 56, wherein the request to register includes a tag identifying a security compliance level.
 63. A method performed by a processor of an edge device configured to support operations of a vehicle, the method comprising: receiving a request from a vehicle component to register with an artificial intelligence processing component of the edge device; sending an acknowledgement of the registration to the vehicle component; receiving a request for a service of the artificial intelligence processing component; and providing, to the vehicle component in response to the request, a response from the service.
 64. The method of claim 63, wherein the artificial intelligence processing component is one of a knowledge management component, a content awareness component, a cognition management component, a situational awareness component, a model-driven engineering component, or a policy management component.
 65. The method of claim 63, wherein the vehicle component is one of a perception component, a decision and control component, or a platform manipulation component.
 66. The method of claim 63, wherein the service is a denormalization service or an output generation service.
 67. The method of claim 63, further comprising: receiving a de-registration request from the vehicle component; and terminating a link between the vehicle component and the artificial intelligence processing component.
 68. The method of claim 63, wherein the edge device operates within an architecture of devices conforming to a standard from a European Telecommunications Standards Institute (ETSI) Experiential Networked Intelligence (ENI) standards group. 